Generative AI and Creative Work

Overall, we find that participants who had access to an AI assistant based on OpenAI's codex-davinci-002 model wrote significantly less secure code than those without access. Additionally, participants with access to an AI assistant were more likely to believe they wrote secure code than those without access to the AI assistant. Furthermore, we find that participants who trusted the AI less and engaged more with the language and format of their prompts (e.g. re-phrasing, adjusting temperature) provided code with fewer security vulnerabilities.

What I am trying to say is that the way search engines work is not because it’s the best way to search, but because of the best technology available in the late 1990s and we are stuck in that paradigm. Maybe there is a better way, but change is hard.

1. Automated content generation: Large language and image AI models can be used to automatically generate content, such as articles, blog posts, or social media posts. This can be a valuable time-saving tool for businesses and professionals who create content on a regular basis.

2. Improved content quality: AI-generated content can be of higher quality than content created by humans, due to the fact that AI models are able to learn from a large amount of data and identify patterns that humans may not be able to see. This can result in more accurate and informative content.

3. Increased content variety: AI models can generate a variety of content types, including text, images, and video. This can help businesses and professionals to create more diverse and interesting content that appeals to a wider range of people.

4. Personalized content: AI models can generate personalized content based on the preferences of individual users. This can help businesses and professionals to create content that is more likely to be of interest to their target audience, and therefore more likely to be read or shared.

The software uses complex machine learning models to predict the next word based on previous word sequences, or the next image based on words describing previous images.

To use generative AI effectively, you still need human involvement at both the beginning and the end of the process.

To start with, a human must enter a prompt into a generative model in order to have it create content. Generally speaking, creative prompts yield creative outputs. “Prompt engineer” is likely to become an established profession, at least until the next generation of even smarter AI emerges.

Then, once a model generates content, it will need to be evaluated and edited carefully by a human.

These generative models are potentially valuable across a number of business functions, but marketing applications are perhaps the most common. Jasper, for example, a marketing-focused version of GPT-3, can produce blogs, social media posts, web copy, sales emails, ads, and other types of customer-facing content.

The consensus on LLM-based code generation is that it works well for such snippets, although the integration of them into a larger program and the integration of the program into a particular technical environment still require human programming capabilities.

LLMs are increasingly being used at the core of conversational AI or chatbots. They potentially offer greater levels of understanding of conversation and context awareness than current conversational technologies.

None of these LLMs is a perfect conversationalist. They are trained on past human content and have a tendency to replicate any racist, sexist, or biased language to which they were exposed in training. Although the companies that created these systems are working on filtering out hate speech, they have not yet been fully successful.

Some companies are exploring the idea of LLM-based knowledge management in conjunction with the leading providers of commercial LLMs. Morgan Stanley, for example, is working with OpenAI’s GPT-3 to fine-tune training on wealth management content, so that financial advisors can both search for existing knowledge within the firm and create tailored content for clients easily. It seems likely that users of such systems will need training or assistance in creating effective prompts, and that the knowledge outputs of the LLMs might still need editing or review before being applied. Assuming that such issues are addressed, however, LLMs could rekindle the field of knowledge management and allow it to scale much more effectively.

Heretofore, however, the creation of deepfakes required a considerable amount of computing skill. Now, however, almost anyone will be able to create them. OpenAI has attempted to control fake images by “watermarking” each DALL-E 2 image with a distinctive symbol. More controls are likely to be required in the future, however — particularly as generative video creation becomes mainstream.

Generative AI also raises numerous questions about what constitutes original and proprietary content. Since the created text and images are not exactly like any previous content, the providers of these systems argue that they belong to their prompt creators. But they are clearly derivative of the previous text and images used to train the models. Needless to say, these technologies will provide substantial work for intellectual property attorneys in the coming years.